Ferragina A, Cipolat-Gotet C, Cecchinato A, Bittante G
Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy.
J Dairy Sci. 2013;96(12):7980-90. doi: 10.3168/jds.2013-7036. Epub 2013 Oct 4.
Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh unprocessed milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the milk. All individual milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm(-1). Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR=0.65). Promising results were obtained for recovered protein (1-VR=0.81), total solids (1-VR=0.86), and energy (1-VR=0.76), whereas recovered fat exhibited a low accuracy (1-VR=0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and milk nutrient recovery, potentially allowing these traits to be enhanced through selection.
奶酪产量是许多国家乳制品行业一项重要的技术特性。本研究的目的是评估对新鲜未加工牛奶样品进行傅里叶变换红外（FTIR）光谱分析以预测奶酪产量和营养成分回收特性的有效性。从个体瑞士褐牛采集的1500毫升牛奶样品中总共获得了1264个模型奶酪。通过实验室奶酪制作程序获得了7个与新奶酪产量相关特性的个体测量值，包括新鲜奶酪产量、总固体奶酪产量以及凝乳中保留的水分，所有这些均以加工牛奶的百分比表示，以及凝乳中营养成分回收（脂肪、蛋白质、总固体和能量）占牛奶中相同营养成分的百分比。所有个体牛奶样品均使用MilkoScan FT6000在5000至900波数×厘米⁻¹的光谱范围内进行分析。每个样品进行两次光谱采集，数据分析前对结果进行平均。拟合了不同的化学计量学模型并进行比较，目的是提高预测这些特性的校准方程的准确性。对于总固体奶酪产量和新鲜奶酪产量获得了最准确的预测，其交叉验证（1-VR）中预测值与测量值之间的决定系数分别为0.95和0.83。对于凝乳中保留的水分获得的结果较差（1-VR = 0.65）。对于回收的蛋白质（1-VR = 0.81）、总固体（1-VR = 0.86）和能量（1-VR = 0.76）获得了有前景的结果，而回收的脂肪准确性较低（1-VR = 0.41）。由于FTIR光谱学是一种快速、廉价、高通量的技术，已用于收集标准牛奶记录数据，这些用于奶酪产量和营养成分回收的FTIR校准突出了该技术在乳制品行业的其他潜在应用，特别是用于监测奶酪制作过程和牛奶支付系统。此外，预测模型可用于为育种机构提供有关奶酪产量和牛奶营养成分回收新表型的信息，有可能通过选择增强这些特性。
